Data Visualization Vs Data Science

 

Data Science refers to the process or art of interpreting data and creating useful information, whereas Data Visualization refers to the representation of data. Although both of them are different but are interlinked with each other, we can say that data visualization is a subset or part of data science. Let’s elaborate on the difference.

 

Basis Of Difference	Data Science	Data Visualization
1. Meaning	Data Science is the study of data and converting it into useful information.	It is the process of translating large data sets into charts, maps, graphs, and other visuals.
2. Data Size	It works on any size of data.	It works on a massive amount of data.
3. Goal	The main goal of data science is to gain knowledge from raw data and analyze it to extract useful information.	The main purpose of data visualization is to visualize data by representing it in pictorial form.
4. Professionals who perform it?	Data scientists, Data Analysts, Mathematicians	Data Scientist, UI/UX
5. Tools & Techniques	Tableau, TensorFlow, BigML, SAS, Apache Hadoop, MATLAB, Apache Spark, Excel, Jupyter, NLTK, Python, etc.	Zoho Analytics, Domo, Sisense, Looker, Qlik Sense, Tableau, SAP, IBM Cognos Analytics, etc.
6. Process	Define business objectives, Collect the data, Data Cleaning, Data Analyzing, Building, and test models, Deploy Models, Monitor and Validate objectives.	Exploration, analysis, synthesis, and presentation.
7. Skills Required	Statistics and algorithms	Data analysis, and plotting techniques.
8. Importance	Almost all organizations require data science to make better decisions.	It helps data scientists to understand the data and how to solve the problem and represent it for providing recommendations.
9. Concept	Data science implies multiple statistical solutions to solving problems.	In this, a data scientist analyses data and represents it to the end result.
10. Uses	It is about training the machine by algorithms.	It is about graphs, plotting, etc based on representation.
11. Examples	Manufacturing Industry: Producers depend on data science to create predictions for product demand.	Philosophy: explaining ideas by visuals depiction Data Visualization by video Cinema: Explaining the movie plot
12. Application In Real-Life	Fraud and Risk Detection Internet Search Targeted Advertising Website Recommendations Healthcare: Medical Image Analysis	Health Care Industries Business Intelligence Military Finance Industry Marketing Real Estate Business

 

 

Conclusion:

Although Data Science and Data Visualization have few differences, both of them are bounded by one major objective of extracting useful information. In data science, lots of techniques, tools, and skills are required to extract useful results whereas, in data visualization, tools are required to represent data in the form of visuals like graphs, charts, etc. The Data Science process is broader than data visualization. So, we can say that data science is a broader term as compared to data visualization. Both terms are different in their entities but data visualization is part of data science. And both are important for almost every organization for better decision-making.

 

Why Is Data Visualization Important In Data Science?

Data visualization is important as it discovers the trends in data. It gives a clear idea of what information means by presenting it in the form of visuals like graphs, charts, maps, etc. This makes data more comprehensible for the human mind and as a result, makes it easier to identify patterns in large datasets.

Data Visualization provides companies with clear insights into untapped information. No matter what field or business it is, data visualization helps all businesses by delivering data in the most efficient way. Data visualization takes the raw data, models it, and extracts the conclusions from it.

There are many reasons why data visualization is important in data science, here are a few listed below:

•       Discovers The Trends In Data: It discovers the trends in data, as data visualization represents data in the form of visuals. It makes it much easier to observe data trends and their patterns.

 

•       Converts Data Into More Interactive: Data Visualization allows the user to obtain a universal view of data by translating data into charts, graphs, usage of colors, shapes, etc. It tells a story to users and makes data interactive.

 

 

•       Explains A-Data Process: It can be used to exhibit data processes from beginning to end. This can be done with the help of different charts.

 

•       Saves Time:  It is a faster tool to gather perceptions from data. Data visualization saves a lot of time by creating insights from translated and easily comprehensible data.

 

 

•       Presenting Data Beautifully: Primary purpose of data visualization is to easily impart information to users along with concentrating on beautifully presenting it so that viewers don’t lose interest in it.

 

All of the reasons mentioned above explain the importance of data visualization in data science. It demonstrates the trends and patterns of the data and presents it beautifully which makes it more appealing to people than just presenting data in the form of rows. Although data visualization is an element of data science, it plays an important role in modifying data and making it interesting, so that all viewers can get accurate messages of information extracted from raw data. At last, data visualization helps in representing data, and data science is extracting useful information by converting raw data by using various tools and skills, which help organizations in making better decisions to either solve problems or either to achieve its objective.